preexperiment_date <- "13 May 2023 11 14AM/All"
postexperiment_date <- "13 May 2023 03 52PM/All"
##--- last fish run in trial ---##
experiment_date <- "13 May 2023 12 30PM/Oxygen"
experiment_date2 <- "13 May 2023 12 30PM/All"
firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) preexperiment_date_asus <- "13 May 2023 11 07AM/All"
postexperiment_date_asus <- "13 May 2023 04 23PM/All"
##--- last fish run in trial ---##
experiment_date_asus <- "13 May 2023 01 13PM/Oxygen"
experiment_date2_asus <- "13 May 2023 01 13PM/All"
firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860
chamber1_asus = 0.04565
chamber2_asus = 0.04573
chamber3_asus = 0.04551
chamber4_asus = 0.04791
Date_tested="2023-05-13"
Clutch = "100"
Male = "CARL249"
Female = "CARL360"
Population = "Arlington reef"
Tank =373
salinity =36
Date_analysed = Sys.Date() Replicate = 1
mass = 0.0005823
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes="check max and rest"
##--- time of trail ---##
experiment_mmr_date <- "13 May 2023 11 50AM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 11 50AM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] 1.572533e-05
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.001569484
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 6 8 9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 14 1 344.7483 -0.02083626 0.993 NA 6346 6579 11769.91
## 2: 17 1 377.8635 -0.02076999 0.989 NA 7825 8058 13389.93
## 3: 18 1 329.4044 -0.01647411 0.987 NA 8319 8553 13930.39
## 4: 19 1 364.8815 -0.01830641 0.991 NA 8812 9045 14469.47
## 5: 20 1 366.0873 -0.01771942 0.987 NA 9306 9539 15010.30
## 6: 21 1 341.7865 -0.01555060 0.985 NA 9799 10032 15549.70
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 12025.09 99.195 93.983 -0.02083626 -0.001207550 -0.01962871 -0.01962871
## 2: 13644.61 99.551 94.084 -0.02076999 -0.001463498 -0.01930649 -0.01930649
## 3: 14186.02 99.538 95.403 -0.01647411 -0.001548974 -0.01492513 -0.01492513
## 4: 14724.33 99.673 95.180 -0.01830641 -0.001634096 -0.01667232 -0.01667232
## 5: 15264.90 99.899 95.834 -0.01771942 -0.001719535 -0.01599988 -0.01599988
## 6: 15805.01 99.750 95.726 -0.01555060 -0.001804825 -0.01374577 -0.01374577
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0486 0.0005823 NA 36 30 1.013253 -0.2124061
## 2: %Air sec 0.0486 0.0005823 NA 36 30 1.013253 -0.2089192
## 3: %Air sec 0.0486 0.0005823 NA 36 30 1.013253 -0.1615077
## 4: %Air sec 0.0486 0.0005823 NA 36 30 1.013253 -0.1804143
## 5: %Air sec 0.0486 0.0005823 NA 36 30 1.013253 -0.1731378
## 6: %Air sec 0.0486 0.0005823 NA 36 30 1.013253 -0.1487457
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -364.7709 NA mgO2/hr/kg -364.7709
## 2: -358.7828 NA mgO2/hr/kg -358.7828
## 3: -277.3617 NA mgO2/hr/kg -277.3617
## 4: -309.8305 NA mgO2/hr/kg -309.8305
## 5: -297.3344 NA mgO2/hr/kg -297.3344
## 6: -255.4451 NA mgO2/hr/kg -255.4451
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 1 | CARL249 | CARL360 | Arlington reef | 373 | 0.0005823 | ch4 | Dell | 0.0486 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 321.6161 | 0.187277 | 0.9894 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.74
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 7 10 11 12 13 14 15 16 18 19 23 24 26 27 28 29
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.41
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 243.4814 -0.061147538 0.9567140 NA 208 261 2541.84
## 2: NA 2 243.4393 -0.061137204 0.9568215 NA 207 260 2540.46
## 3: NA 3 240.4646 -0.061084948 0.9926824 NA 94 147 2409.68
## 4: NA 4 240.4602 -0.061084638 0.9926865 NA 93 146 2408.57
## 5: NA 5 240.3756 -0.061046829 0.9925918 NA 95 148 2410.77
## ---
## 205: NA 205 106.4797 -0.007403344 0.7140865 NA 167 220 2494.47
## 206: NA 206 105.9608 -0.007205789 0.7566911 NA 171 224 2499.19
## 207: NA 207 105.6227 -0.007067061 0.7438188 NA 168 221 2495.56
## 208: NA 208 105.3331 -0.006956580 0.7550250 NA 170 223 2498.04
## 209: NA 209 105.2373 -0.006917017 0.7554722 NA 169 222 2496.65
## endtime oxy endoxy rate
## 1: 2601.84 87.704 84.781 -0.061147538
## 2: 2600.46 87.693 84.755 -0.061137204
## 3: 2469.68 93.201 89.698 -0.061084948
## 4: 2468.57 93.237 89.740 -0.061084638
## 5: 2470.77 93.134 89.660 -0.061046829
## ---
## 205: 2554.47 88.446 87.495 -0.007403344
## 206: 2559.19 88.063 87.396 -0.007205789
## 207: 2555.56 88.357 87.446 -0.007067061
## 208: 2558.04 88.181 87.421 -0.006956580
## 209: 2556.65 88.295 87.431 -0.006917017
##
## Regressions : 209 | Results : 209 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 209 adjusted rate(s):
## Rate : -0.06114754
## Adjustment : 1.572533e-05
## Adjusted Rate : -0.06116326
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 64 rate(s) removed, 145 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 144 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 243.4814 -0.06114754 0.956714 NA 208 261 2541.84
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 2601.84 87.704 84.781 -0.06114754 1.572533e-05 -0.06116326 -0.06116326
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0486 0.0005823 NA 36 30 1.013253 -0.6618594
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1136.63 NA mgO2/hr/kg -1136.63
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 1 | CARL249 | CARL360 | Arlington reef | 373 | 0.0005823 | ch4 | Dell | 0.0486 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 321.6161 | 0.187277 | 0.9894 | 1136.63 | 0.6618594 | 0.956714 | 815.0135 | 0.4745824 | check max and rest |
## Rows: 100 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Replicate = 2
mass = 0.0004707
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes="max and rest seems low"
##--- time of trail ---##
experiment_mmr_date <- "13 May 2023 12 19PM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 12 19PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.0005855578
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.002506235
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 6 8 9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 9 1 263.3727 -0.01812897 0.993 NA 3890 4119 9069.71
## 2: 12 1 299.9328 -0.01872412 0.971 NA 5360 5593 10690.04
## 3: 13 1 301.9229 -0.01801757 0.991 NA 5853 6086 11230.32
## 4: 15 1 321.8125 -0.01804544 0.995 NA 6839 7073 12310.07
## 5: 19 1 346.6633 -0.01707441 0.983 NA 8812 9045 14469.47
## 6: 20 1 375.2195 -0.01833765 0.995 NA 9306 9539 15010.30
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 9324.80 98.710 94.078 -0.01812897 -0.001550731 -0.01657823 -0.01657823
## 2: 10945.31 99.387 94.513 -0.01872412 -0.001860969 -0.01686315 -0.01686315
## 3: 11485.43 99.454 94.653 -0.01801757 -0.001964393 -0.01605318 -0.01605318
## 4: 12565.60 99.689 95.169 -0.01804544 -0.002171157 -0.01587428 -0.01587428
## 5: 14724.33 99.442 95.348 -0.01707441 -0.002584521 -0.01448989 -0.01448989
## 6: 15264.90 99.771 95.213 -0.01833765 -0.002688040 -0.01564961 -0.01564961
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.0004707 NA 36 30 1.013253 -0.1837151
## 2: %Air sec 0.04977 0.0004707 NA 36 30 1.013253 -0.1868724
## 3: %Air sec 0.04977 0.0004707 NA 36 30 1.013253 -0.1778965
## 4: %Air sec 0.04977 0.0004707 NA 36 30 1.013253 -0.1759141
## 5: %Air sec 0.04977 0.0004707 NA 36 30 1.013253 -0.1605727
## 6: %Air sec 0.04977 0.0004707 NA 36 30 1.013253 -0.1734243
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -390.3018 NA mgO2/hr/kg -390.3018
## 2: -397.0097 NA mgO2/hr/kg -397.0097
## 3: -377.9403 NA mgO2/hr/kg -377.9403
## 4: -373.7286 NA mgO2/hr/kg -373.7286
## 5: -341.1359 NA mgO2/hr/kg -341.1359
## 6: -368.4392 NA mgO2/hr/kg -368.4392
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 2 | CARL249 | CARL360 | Arlington reef | 373 | 0.0004707 | ch3 | Dell | 0.04977 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 381.4839 | 0.1795645 | 0.989 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.74
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 9 10 11 12 13 15 17 18 20 21 24 25 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.38
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 338.8036 -0.05826840 0.9947858 NA 72 126 4145.55
## 2: NA 2 338.5456 -0.05820761 0.9946429 NA 71 125 4144.45
## 3: NA 3 338.3196 -0.05815184 0.9945928 NA 73 127 4146.77
## 4: NA 4 337.6773 -0.05800075 0.9940756 NA 70 124 4143.27
## 5: NA 5 337.4374 -0.05793982 0.9941715 NA 74 128 4147.86
## ---
## 207: NA 207 177.0633 -0.01920212 0.9709916 NA 13 67 4079.19
## 208: NA 208 177.0611 -0.01919827 0.9710164 NA 10 64 4075.83
## 209: NA 209 176.9497 -0.01917323 0.9714974 NA 12 66 4078.09
## 210: NA 210 176.9193 -0.01916876 0.9675966 NA 14 68 4080.56
## 211: NA 211 176.8531 -0.01914863 0.9718680 NA 11 65 4076.92
## endtime oxy endoxy rate
## 1: 4205.55 97.150 93.845 -0.05826840
## 2: 4204.45 97.149 93.890 -0.05820761
## 3: 4206.77 97.170 93.795 -0.05815184
## 4: 4203.27 97.192 93.937 -0.05800075
## 5: 4207.86 97.116 93.781 -0.05793982
## ---
## 207: 4139.19 98.884 97.442 -0.01920212
## 208: 4135.83 98.970 97.589 -0.01919827
## 209: 4138.09 98.897 97.484 -0.01917323
## 210: 4140.56 98.850 97.376 -0.01916876
## 211: 4136.92 98.934 97.540 -0.01914863
##
## Regressions : 211 | Results : 211 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 211 adjusted rate(s):
## Rate : -0.0582684
## Adjustment : -0.0005855578
## Adjusted Rate : -0.05768285
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 211 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 210 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 338.8036 -0.0582684 0.9947858 NA 72 126 4145.55
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 4205.55 97.15 93.845 -0.0582684 -0.0005855578 -0.05768285 -0.05768285
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.0004707 NA 36 30 1.013253 -0.6392241
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1358.029 NA mgO2/hr/kg -1358.029
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 2 | CARL249 | CARL360 | Arlington reef | 373 | 0.0004707 | ch3 | Dell | 0.04977 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 381.4839 | 0.1795645 | 0.989 | 1358.029 | 0.6392241 | 0.9947858 | 976.5448 | 0.4596597 | max and rest seems low |
## Rows: 101 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Replicate = 3
mass = 0.0005800
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "13 May 2023 12 10PM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 12 10PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] 0.0001214543
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.001449414
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 6 8 9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 1 1 190.6061 -0.01959003 0.965 NA 40 263 4749.85
## 2: 4 1 267.2548 -0.02632660 0.977 NA 1461 1689 6370.38
## 3: 5 1 251.7973 -0.02201686 0.984 NA 1946 2177 6909.90
## 4: 10 1 328.7728 -0.02405259 0.991 NA 4377 4608 9609.37
## 5: 14 1 421.2917 -0.02742542 0.993 NA 6346 6579 11769.91
## 6: 19 1 471.2868 -0.02574880 0.994 NA 8812 9045 14469.47
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 5005.67 98.117 92.054 -0.01959003 8.437034e-06 -0.01959846 -0.01959846
## 2: 6625.01 98.823 92.556 -0.02632660 -2.452217e-04 -0.02608138 -0.02608138
## 3: 7165.42 99.243 93.820 -0.02201686 -3.297725e-04 -0.02168709 -0.02168709
## 4: 9864.74 97.622 91.235 -0.02405259 -7.524592e-04 -0.02330014 -0.02330014
## 5: 12025.09 98.555 91.833 -0.02742542 -1.090754e-03 -0.02633466 -0.02633466
## 6: 14724.33 98.661 92.408 -0.02574880 -1.513442e-03 -0.02423535 -0.02423535
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.00058 NA 36 30 1.013253 -0.2004275
## 2: %Air sec 0.04593 0.00058 NA 36 30 1.013253 -0.2667263
## 3: %Air sec 0.04593 0.00058 NA 36 30 1.013253 -0.2217872
## 4: %Air sec 0.04593 0.00058 NA 36 30 1.013253 -0.2382834
## 5: %Air sec 0.04593 0.00058 NA 36 30 1.013253 -0.2693165
## 6: %Air sec 0.04593 0.00058 NA 36 30 1.013253 -0.2478476
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -345.5647 NA mgO2/hr/kg -345.5647
## 2: -459.8729 NA mgO2/hr/kg -459.8729
## 3: -382.3918 NA mgO2/hr/kg -382.3918
## 4: -410.8334 NA mgO2/hr/kg -410.8334
## 5: -464.3389 NA mgO2/hr/kg -464.3389
## 6: -427.3234 NA mgO2/hr/kg -427.3234
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 3 | CARL249 | CARL360 | Arlington reef | 373 | 0.00058 | ch2 | Dell | 0.04593 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 428.9521 | 0.2487922 | 0.9878 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.74
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 6 7 11 12 14 16 17 19 20 21 22 23 24 25 26 28 30
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.44
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 348.3497 -0.06997782 0.9728653 NA 1 55 3551.86
## 2: NA 2 345.4258 -0.06916086 0.9713413 NA 2 56 3553.03
## 3: NA 3 341.9077 -0.06817865 0.9698283 NA 3 57 3554.14
## 4: NA 4 337.2930 -0.06689179 0.9692806 NA 4 58 3555.24
## 5: NA 5 332.1048 -0.06544582 0.9694987 NA 5 59 3556.39
## ---
## 209: NA 209 136.9087 -0.01224002 0.9287390 NA 111 165 3675.94
## 210: NA 210 136.1952 -0.01204309 0.9272251 NA 107 161 3671.42
## 211: NA 211 135.9820 -0.01198924 0.9329675 NA 110 164 3674.76
## 212: NA 212 135.4332 -0.01183877 0.9372692 NA 108 162 3672.57
## 213: NA 213 135.2900 -0.01180141 0.9389441 NA 109 163 3673.66
## endtime oxy endoxy rate
## 1: 3611.86 99.935 95.927 -0.06997782
## 2: 3613.03 99.897 95.838 -0.06916086
## 3: 3614.14 99.873 95.805 -0.06817865
## 4: 3615.24 99.925 95.762 -0.06689179
## 5: 3616.39 99.893 95.726 -0.06544582
## ---
## 209: 3735.94 91.976 91.049 -0.01224002
## 210: 3731.42 92.298 91.218 -0.01204309
## 211: 3734.76 92.076 91.058 -0.01198924
## 212: 3732.57 92.238 91.152 -0.01183877
## 213: 3733.66 92.163 91.113 -0.01180141
##
## Regressions : 213 | Results : 213 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 213 adjusted rate(s):
## Rate : -0.06997782
## Adjustment : 0.0001214543
## Adjusted Rate : -0.07009928
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 33 rate(s) removed, 180 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 179 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 348.3497 -0.06997782 0.9728653 NA 1 55 3551.86
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 3611.86 99.935 95.927 -0.06997782 0.0001214543 -0.07009928 -0.07009928
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.00058 NA 36 30 1.013253 -0.7168839
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1236.007 NA mgO2/hr/kg -1236.007
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 3 | CARL249 | CARL360 | Arlington reef | 373 | 0.00058 | ch2 | Dell | 0.04593 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 428.9521 | 0.2487922 | 0.9878 | 1236.007 | 0.7168839 | 0.9728653 | 807.0547 | 0.4680917 |
## Rows: 102 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Replicate = 4
mass = 0.0004503
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "13 May 2023 12 30PM/Oxygen"
experiment_mmr_date2 <- "13 May 2023 12 30PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -5.852136e-05
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.002269307
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 6 8 9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 14 1 335.0586 -0.02018689 0.998 NA 6346 6579 11769.91
## 2: 16 1 400.6679 -0.02353068 0.991 NA 7333 7566 12849.99
## 3: 17 1 435.5064 -0.02516601 0.999 NA 7825 8058 13389.93
## 4: 19 1 439.9248 -0.02359002 0.991 NA 8812 9045 14469.47
## 5: 20 1 487.4384 -0.02587190 0.996 NA 9306 9539 15010.30
## 6: 21 1 428.0032 -0.02118516 0.974 NA 9799 10032 15549.70
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 12025.09 97.706 92.119 -0.02018689 -0.001764542 -0.01842235 -0.01842235
## 2: 13104.83 97.875 92.054 -0.02353068 -0.002002525 -0.02152815 -0.02152815
## 3: 13644.61 98.583 92.060 -0.02516601 -0.002121496 -0.02304451 -0.02304451
## 4: 14724.33 98.663 92.677 -0.02359002 -0.002359418 -0.02123060 -0.02123060
## 5: 15264.90 99.174 92.359 -0.02587190 -0.002478573 -0.02339333 -0.02339333
## 6: 15805.01 99.053 93.282 -0.02118516 -0.002597521 -0.01858764 -0.01858764
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0004503 NA 36 30 1.013253 -0.1907378
## 2: %Air sec 0.0465 0.0004503 NA 36 30 1.013253 -0.2228941
## 3: %Air sec 0.0465 0.0004503 NA 36 30 1.013253 -0.2385939
## 4: %Air sec 0.0465 0.0004503 NA 36 30 1.013253 -0.2198134
## 5: %Air sec 0.0465 0.0004503 NA 36 30 1.013253 -0.2422054
## 6: %Air sec 0.0465 0.0004503 NA 36 30 1.013253 -0.1924492
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -423.5795 NA mgO2/hr/kg -423.5795
## 2: -494.9902 NA mgO2/hr/kg -494.9902
## 3: -529.8554 NA mgO2/hr/kg -529.8554
## 4: -488.1487 NA mgO2/hr/kg -488.1487
## 5: -537.8756 NA mgO2/hr/kg -537.8756
## 6: -427.3799 NA mgO2/hr/kg -427.3799
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 4 | CARL249 | CARL360 | Arlington reef | 373 | 0.0004503 | ch1 | Dell | 0.0465 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 495.65 | 0.2231912 | 0.9902 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 10.44
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 6 8 9 10 11 12 13 14 15 16 18 19 20 21 22 23 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.48
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 432.4408 -0.06999253 0.9901759 NA 84 137 4799.85
## 2: NA 2 432.4127 -0.06998535 0.9901487 NA 85 138 4800.94
## 3: NA 3 432.0604 -0.06991107 0.9899415 NA 86 139 4802.03
## 4: NA 4 432.0059 -0.06990404 0.9899182 NA 83 136 4798.51
## 5: NA 5 431.8879 -0.06987419 0.9898539 NA 87 140 4803.36
## ---
## 205: NA 205 207.1476 -0.02310715 0.9724506 NA 37 90 4746.96
## 206: NA 206 206.2229 -0.02291787 0.9734921 NA 41 94 4751.44
## 207: NA 207 205.6453 -0.02279368 0.9762790 NA 38 91 4748.14
## 208: NA 208 205.2530 -0.02271380 0.9769990 NA 40 93 4750.31
## 209: NA 209 204.7465 -0.02260664 0.9788831 NA 39 92 4749.23
## endtime oxy endoxy rate
## 1: 4859.85 96.293 92.465 -0.06999253
## 2: 4860.94 96.237 92.393 -0.06998535
## 3: 4862.03 96.175 92.345 -0.06991107
## 4: 4858.51 96.307 92.534 -0.06990404
## 5: 4863.36 96.116 92.228 -0.06987419
## ---
## 205: 4806.96 97.668 96.053 -0.02310715
## 206: 4811.44 97.441 95.750 -0.02291787
## 207: 4808.14 97.641 96.010 -0.02279368
## 208: 4810.31 97.489 95.816 -0.02271380
## 209: 4809.23 97.579 95.944 -0.02260664
##
## Regressions : 209 | Results : 209 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 209 adjusted rate(s):
## Rate : -0.06999253
## Adjustment : -5.852136e-05
## Adjusted Rate : -0.06993401
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 19 rate(s) removed, 190 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 189 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 432.4408 -0.06999253 0.9901759 NA 84 137 4799.85
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 4859.85 96.293 92.465 -0.06999253 -5.852136e-05 -0.06993401 -0.06993401
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0004503 NA 36 30 1.013253 -0.7240695
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1607.971 NA mgO2/hr/kg -1607.971
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 4 | CARL249 | CARL360 | Arlington reef | 373 | 0.0004503 | ch1 | Dell | 0.0465 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 495.65 | 0.2231912 | 0.9902 | 1607.971 | 0.7240695 | 0.9901759 | 1112.321 | 0.5008783 |
## Rows: 103 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Replicate = 5
mass = 0.0007720
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "13 May 2023 12 44PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 12 44PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] 0.001076512
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.0008635787
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 7 1 382.6597 -0.02601808 0.980 NA 2429 2618 10928.37
## 2: 11 1 485.5951 -0.02957571 0.985 NA 4020 4209 13089.28
## 3: 12 1 482.6028 -0.02818016 0.994 NA 4419 4608 13628.68
## 4: 17 1 552.0899 -0.02775387 0.995 NA 6411 6600 16328.50
## 5: 18 1 640.2116 -0.03207079 0.984 NA 6810 6999 16869.11
## 6: 19 1 611.8451 -0.02951783 0.994 NA 7209 7398 17409.36
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 11183.62 98.245 91.654 -0.02601808 -0.0002037005 -0.02581438 -0.02581438
## 2: 13344.85 98.469 91.510 -0.02957571 -0.0007153763 -0.02886033 -0.02886033
## 3: 13884.35 98.513 90.932 -0.02818016 -0.0008431017 -0.02733706 -0.02733706
## 4: 16584.52 98.875 91.372 -0.02775387 -0.0014823785 -0.02627149 -0.02627149
## 5: 17124.92 99.157 90.644 -0.03207079 -0.0016103537 -0.03046044 -0.03046044
## 6: 17665.09 98.091 90.310 -0.02951783 -0.0017382590 -0.02777957 -0.02777957
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.000772 NA 36 30 1.013253 -0.2753763
## 2: %Air sec 0.04791 0.000772 NA 36 30 1.013253 -0.3078693
## 3: %Air sec 0.04791 0.000772 NA 36 30 1.013253 -0.2916196
## 4: %Air sec 0.04791 0.000772 NA 36 30 1.013253 -0.2802527
## 5: %Air sec 0.04791 0.000772 NA 36 30 1.013253 -0.3249385
## 6: %Air sec 0.04791 0.000772 NA 36 30 1.013253 -0.2963402
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -356.7051 NA mgO2/hr/kg -356.7051
## 2: -398.7944 NA mgO2/hr/kg -398.7944
## 3: -377.7457 NA mgO2/hr/kg -377.7457
## 4: -363.0216 NA mgO2/hr/kg -363.0216
## 5: -420.9048 NA mgO2/hr/kg -420.9048
## 6: -383.8603 NA mgO2/hr/kg -383.8603
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 5 | CARL249 | CARL360 | Arlington reef | 373 | 0.000772 | ch4 | Asus | 0.04791 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 388.8654 | 0.3002041 | 0.9904 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 4.11
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.41
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 583.8845 -0.082489692 0.9960442 NA 12 57 5915.58
## 2: NA 2 583.7551 -0.082469019 0.9960146 NA 11 56 5914.24
## 3: NA 3 581.9935 -0.082173449 0.9956812 NA 10 55 5912.90
## 4: NA 4 581.8203 -0.082141060 0.9953315 NA 13 58 5916.91
## 5: NA 5 578.8979 -0.081653326 0.9950795 NA 9 54 5911.53
## ---
## 174: NA 174 149.4734 -0.009755240 0.5563961 NA 52 97 5969.60
## 175: NA 175 149.0420 -0.009672054 0.5707880 NA 48 93 5964.18
## 176: NA 176 145.7005 -0.009123153 0.5890778 NA 51 96 5968.27
## 177: NA 177 144.9184 -0.008986866 0.5982235 NA 49 94 5965.55
## 178: NA 178 143.7919 -0.008801890 0.6105300 NA 50 95 5966.90
## endtime oxy endoxy rate
## 1: 5975.58 95.822 91.100 -0.082489692
## 2: 5974.24 95.899 91.107 -0.082469019
## 3: 5972.90 95.940 91.174 -0.082173449
## 4: 5976.91 95.746 91.141 -0.082141060
## 5: 5971.53 96.022 91.316 -0.081653326
## ---
## 174: 6029.60 91.532 90.099 -0.009755240
## 175: 6024.18 91.922 90.717 -0.009672054
## 176: 6028.27 91.603 90.232 -0.009123153
## 177: 6025.55 91.844 90.566 -0.008986866
## 178: 6026.90 91.722 90.403 -0.008801890
##
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 178 adjusted rate(s):
## Rate : -0.08248969
## Adjustment : 0.001076512
## Adjusted Rate : -0.0835662
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 54 rate(s) removed, 124 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 123 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 583.8845 -0.08248969 0.9960442 NA 12 57 5915.58
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 5975.58 95.822 91.1 -0.08248969 0.001076512 -0.0835662 -0.0835662
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.000772 NA 36 30 1.013253 -0.8914473
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1154.725 NA mgO2/hr/kg -1154.725
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 5 | CARL249 | CARL360 | Arlington reef | 373 | 0.000772 | ch4 | Asus | 0.04791 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 388.8654 | 0.3002041 | 0.9904 | 1154.725 | 0.8914473 | 0.9960442 | 765.8591 | 0.5912433 | ||
| ### Expor | ting data |
## Rows: 104 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Replicate = 6
mass = 0.0005870
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "13 May 2023 12 53PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 12 53PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] 0.0002177651
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.002677291
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 8 1 401.8909 -0.02662167 0.993 NA 2829 3011 11468.76
## 2: 9 1 416.4061 -0.02662055 0.990 NA 3229 3411 12009.08
## 3: 11 1 436.3074 -0.02587229 0.979 NA 4020 4202 13089.28
## 4: 13 1 441.8821 -0.02428353 0.978 NA 4816 4997 14169.03
## 5: 18 1 396.2199 -0.01769467 0.974 NA 6810 6991 16869.11
## 6: 21 1 437.7328 -0.01836936 0.986 NA 8007 8188 18489.32
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 11714.72 96.685 90.392 -0.02662167 -0.001881889 -0.02473978 -0.02473978
## 2: 12255.06 97.039 90.697 -0.02662055 -0.002072795 -0.02454775 -0.02454775
## 3: 13335.40 98.054 91.241 -0.02587229 -0.002454470 -0.02341782 -0.02341782
## 4: 14414.02 97.756 92.139 -0.02428353 -0.002835761 -0.02144777 -0.02144777
## 5: 17114.09 98.003 93.702 -0.01769467 -0.003789736 -0.01390493 -0.01390493
## 6: 18734.12 97.999 93.293 -0.01836936 -0.004362147 -0.01400722 -0.01400722
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04551 0.000587 NA 36 30 1.013253 -0.2506926
## 2: %Air sec 0.04551 0.000587 NA 36 30 1.013253 -0.2487467
## 3: %Air sec 0.04551 0.000587 NA 36 30 1.013253 -0.2372969
## 4: %Air sec 0.04551 0.000587 NA 36 30 1.013253 -0.2173341
## 5: %Air sec 0.04551 0.000587 NA 36 30 1.013253 -0.1409011
## 6: %Air sec 0.04551 0.000587 NA 36 30 1.013253 -0.1419376
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -427.0743 NA mgO2/hr/kg -427.0743
## 2: -423.7593 NA mgO2/hr/kg -423.7593
## 3: -404.2537 NA mgO2/hr/kg -404.2537
## 4: -370.2454 NA mgO2/hr/kg -370.2454
## 5: -240.0360 NA mgO2/hr/kg -240.0360
## 6: -241.8018 NA mgO2/hr/kg -241.8018
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 6 | CARL249 | CARL360 | Arlington reef | 373 | 0.000587 | ch3 | Asus | 0.04551 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 373.4269 | 0.2192016 | 0.9852 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 4.11
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 12 13 14 15 17 18 19 21 22 23 24
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.42
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 573.3481 -0.07301918 0.9856640 NA 144 189 6618.11
## 2: NA 2 573.1975 -0.07299836 0.9855884 NA 143 188 6616.76
## 3: NA 3 571.8223 -0.07279344 0.9847715 NA 142 187 6615.41
## 4: NA 4 571.5421 -0.07274603 0.9847927 NA 145 190 6619.46
## 5: NA 5 569.2432 -0.07240735 0.9831847 NA 141 186 6614.06
## ---
## 173: NA 173 337.4840 -0.03737809 0.9868192 NA 89 134 6543.83
## 174: NA 174 335.9278 -0.03714044 0.9881461 NA 88 133 6542.50
## 175: NA 175 335.7616 -0.03711301 0.9881633 NA 85 130 6538.42
## 176: NA 176 334.7633 -0.03696254 0.9889250 NA 87 132 6541.14
## 177: NA 177 334.5951 -0.03693626 0.9890376 NA 86 131 6539.77
## endtime oxy endoxy rate
## 1: 6678.11 89.866 85.982 -0.07301918
## 2: 6676.76 89.882 86.052 -0.07299836
## 3: 6675.41 89.911 86.111 -0.07279344
## 4: 6679.46 89.887 85.922 -0.07274603
## 5: 6674.06 89.963 86.204 -0.07240735
## ---
## 173: 6603.83 92.999 90.463 -0.03737809
## 174: 6602.50 93.033 90.566 -0.03714044
## 175: 6598.42 93.351 90.804 -0.03711301
## 176: 6601.14 93.114 90.664 -0.03696254
## 177: 6599.77 93.237 90.745 -0.03693626
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.07301918
## Adjustment : 0.0002177651
## Adjusted Rate : -0.07323695
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 573.3481 -0.07301918 0.985664 NA 144 189 6618.11
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6678.11 89.866 85.982 -0.07301918 0.0002177651 -0.07323695 -0.07323695
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04551 0.000587 NA 36 30 1.013253 -0.742123
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1264.264 NA mgO2/hr/kg -1264.264
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 6 | CARL249 | CARL360 | Arlington reef | 373 | 0.000587 | ch3 | Asus | 0.04551 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 373.4269 | 0.2192016 | 0.9852 | 1264.264 | 0.742123 | 0.985664 | 890.8372 | 0.5229214 | ||
| ### Expor | ting data |
## Rows: 105 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Replicate = 7
mass = 0.0006606
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "13 May 2023 01 03PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 01 03PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] 0.0008508406
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.00206665
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 6 1 324.7317 -0.02190726 0.993 NA 2030 2211 10389.22
## 2: 9 1 368.6146 -0.02253713 0.992 NA 3229 3411 12009.08
## 3: 10 1 375.9054 -0.02214872 0.995 NA 3629 3811 12549.34
## 4: 11 1 405.3318 -0.02345801 0.997 NA 4020 4202 13089.28
## 5: 14 1 423.6132 -0.02212574 0.994 NA 5215 5396 14709.16
## 6: 20 1 513.4955 -0.02313632 0.992 NA 7608 7789 17949.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 10633.82 97.223 91.920 -0.02190726 -0.0008804697 -0.02102679 -0.02102679
## 2: 12255.06 97.882 92.163 -0.02253713 -0.0014574699 -0.02107966 -0.02107966
## 3: 12795.28 97.858 92.175 -0.02214872 -0.0016498235 -0.02049890 -0.02049890
## 4: 13335.40 98.157 92.276 -0.02345801 -0.0018421023 -0.02161591 -0.02161591
## 5: 14954.27 98.251 92.485 -0.02212574 -0.0024186841 -0.01970706 -0.01970706
## 6: 18194.02 98.279 92.660 -0.02313632 -0.0035722323 -0.01956409 -0.01956409
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04573 0.0006606 NA 36 30 1.013253 -0.2140982
## 2: %Air sec 0.04573 0.0006606 NA 36 30 1.013253 -0.2146365
## 3: %Air sec 0.04573 0.0006606 NA 36 30 1.013253 -0.2087231
## 4: %Air sec 0.04573 0.0006606 NA 36 30 1.013253 -0.2200967
## 5: %Air sec 0.04573 0.0006606 NA 36 30 1.013253 -0.2006605
## 6: %Air sec 0.04573 0.0006606 NA 36 30 1.013253 -0.1992047
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -324.0966 NA mgO2/hr/kg -324.0966
## 2: -324.9114 NA mgO2/hr/kg -324.9114
## 3: -315.9599 NA mgO2/hr/kg -315.9599
## 4: -333.1769 NA mgO2/hr/kg -333.1769
## 5: -303.7549 NA mgO2/hr/kg -303.7549
## 6: -301.5512 NA mgO2/hr/kg -301.5512
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 7 | CARL249 | CARL360 | Arlington reef | 373 | 0.0006606 | ch2 | Asus | 0.04573 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 320.38 | 0.211643 | 0.9942 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 4.11
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = 5259, # custom
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 10 11 12 13 15 16 18 19 20 21 24 25 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.38
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 704.8510 -0.08633115 0.9981164 NA 13 58 7032.88
## 2: NA 2 704.2120 -0.08624074 0.9981195 NA 14 59 7034.24
## 3: NA 3 703.6810 -0.08616544 0.9981203 NA 12 57 7031.53
## 4: NA 4 703.0230 -0.08607205 0.9981423 NA 11 56 7030.19
## 5: NA 5 702.1531 -0.08594941 0.9981398 NA 15 60 7035.60
## ---
## 27: NA 27 685.5334 -0.08359882 0.9979191 NA 27 72 7051.79
## 28: NA 28 684.1802 -0.08340760 0.9978620 NA 28 73 7053.15
## 29: NA 29 682.4174 -0.08315842 0.9977262 NA 29 74 7054.51
## 30: NA 30 679.6539 -0.08276766 0.9972590 NA 30 75 7055.87
## 31: NA 31 676.0106 -0.08225244 0.9963343 NA 31 76 7057.20
## endtime oxy endoxy rate
## 1: 7092.88 97.724 92.472 -0.08633115
## 2: 7094.24 97.668 92.410 -0.08624074
## 3: 7091.53 97.879 92.586 -0.08616544
## 4: 7090.19 97.982 92.680 -0.08607205
## 5: 7095.60 97.594 92.340 -0.08594941
## ---
## 27: 7111.79 96.129 91.016 -0.08359882
## 28: 7113.15 95.975 90.956 -0.08340760
## 29: 7114.51 95.844 90.890 -0.08315842
## 30: 7115.87 95.689 90.890 -0.08276766
## 31: 7117.20 95.541 90.888 -0.08225244
##
## Regressions : 31 | Results : 31 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 31 adjusted rate(s):
## Rate : -0.08633115
## Adjustment : 0.0008508406
## Adjusted Rate : -0.08718199
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 31 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 30 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 704.851 -0.08633115 0.9981164 NA 13 58 7032.88
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7092.88 97.724 92.472 -0.08633115 0.0008508406 -0.08718199 -0.08718199
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04573 0.0006606 NA 36 30 1.013253 -0.8877012
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1343.78 NA mgO2/hr/kg -1343.78
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 7 | CARL249 | CARL360 | Arlington reef | 373 | 0.0006606 | ch2 | Asus | 0.04573 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 320.38 | 0.211643 | 0.9942 | 1343.78 | 0.8877012 | 0.9981164 | 1023.4 | 0.6760582 | ||
| ### Expor | ting data |
## Rows: 106 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Replicate = 8
mass = 0.0005530
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "13 May 2023 01 13PM/Oxygen"
experiment_mmr_date2_asus <- "13 May 2023 01 13PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.001065898
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
## [1] -0.0002845502
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 11 12 13 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 12 1 293.9377 -0.01430860 0.977 NA 4419 4600 13628.68
## 2: 13 1 298.5665 -0.01407149 0.983 NA 4816 4997 14169.03
## 3: 15 1 301.6732 -0.01327192 0.969 NA 5614 5795 15249.57
## 4: 16 1 306.9796 -0.01315136 0.970 NA 6012 6193 15788.58
## 5: 17 1 329.1421 -0.01406912 0.971 NA 6411 6592 16328.50
## 6: 20 1 334.3882 -0.01312230 0.980 NA 7608 7789 17949.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 13873.54 98.673 95.210 -0.01430860 -2.933125e-04 -0.01401529 -0.01401529
## 2: 14414.02 98.872 95.499 -0.01407149 -2.417806e-04 -0.01382971 -0.01382971
## 3: 15494.87 98.889 95.631 -0.01327192 -1.387298e-04 -0.01313319 -0.01313319
## 4: 16033.31 98.917 95.882 -0.01315136 -8.735914e-05 -0.01306400 -0.01306400
## 5: 16573.66 98.928 95.608 -0.01406912 -3.585400e-05 -0.01403327 -0.01403327
## 6: 18194.02 98.519 95.353 -0.01312230 1.186672e-04 -0.01324096 -0.01324096
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.000553 NA 36 30 1.013253 -0.1424563
## 2: %Air sec 0.04565 0.000553 NA 36 30 1.013253 -0.1405700
## 3: %Air sec 0.04565 0.000553 NA 36 30 1.013253 -0.1334903
## 4: %Air sec 0.04565 0.000553 NA 36 30 1.013253 -0.1327871
## 5: %Air sec 0.04565 0.000553 NA 36 30 1.013253 -0.1426390
## 6: %Air sec 0.04565 0.000553 NA 36 30 1.013253 -0.1345858
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -257.6064 NA mgO2/hr/kg -257.6064
## 2: -254.1954 NA mgO2/hr/kg -254.1954
## 3: -241.3930 NA mgO2/hr/kg -241.3930
## 4: -240.1213 NA mgO2/hr/kg -240.1213
## 5: -257.9368 NA mgO2/hr/kg -257.9368
## 6: -243.3740 NA mgO2/hr/kg -243.3740
## -----------------------------------------
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 8 | CARL249 | CARL360 | Arlington reef | 373 | 0.000553 | ch1 | Asus | 0.04565 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 250.9011 | 0.1387483 | 0.976 |
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 13.07
## -----------------------------------------
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18 19 20 21 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.44
## -----------------------------------------
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 522.1141 -0.05511474 0.9969994 NA 114 159 7797.94
## 2: NA 2 521.9780 -0.05509777 0.9969680 NA 113 158 7796.57
## 3: NA 3 521.9730 -0.05509616 0.9969620 NA 115 160 7799.29
## 4: NA 4 521.5660 -0.05504551 0.9968818 NA 112 157 7795.21
## 5: NA 5 521.3839 -0.05502055 0.9968495 NA 116 161 7800.64
## ---
## 173: NA 173 302.8310 -0.02727485 0.9776066 NA 173 218 7877.64
## 174: NA 174 296.7104 -0.02650130 0.9809523 NA 174 219 7879.01
## 175: NA 175 291.7542 -0.02587502 0.9831980 NA 175 220 7880.35
## 176: NA 176 288.6302 -0.02548021 0.9831713 NA 176 221 7881.69
## 177: NA 177 286.4035 -0.02519895 0.9834991 NA 177 222 7883.03
## endtime oxy endoxy rate
## 1: 7857.94 92.307 89.093 -0.05511474
## 2: 7856.57 92.373 89.155 -0.05509777
## 3: 7859.29 92.219 89.056 -0.05509616
## 4: 7855.21 92.466 89.187 -0.05504551
## 5: 7860.64 92.194 88.967 -0.05502055
## ---
## 173: 7937.64 88.270 86.424 -0.02727485
## 174: 7939.01 88.186 86.416 -0.02650130
## 175: 7940.35 88.079 86.379 -0.02587502
## 176: 7941.69 87.934 86.346 -0.02548021
## 177: 7943.03 87.875 86.283 -0.02519895
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------
## adjust_rate: Rate adjustments applied using "mean" method.
##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.05511474
## Adjustment : -0.001065898
## Adjusted Rate : -0.05404884
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----
##
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 522.1141 -0.05511474 0.9969994 NA 114 159 7797.94
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7857.94 92.307 89.093 -0.05511474 -0.001065898 -0.05404884 -0.05404884
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.000553 NA 36 30 1.013253 -0.5493713
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -993.4382 NA mgO2/hr/kg -993.4382
## -----------------------------------------
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 100 | 8 | CARL249 | CARL360 | Arlington reef | 373 | 0.000553 | ch1 | Asus | 0.04565 | 2023-05-13 | 2024-06-14 | good/good | 36 | 30 | 250.9011 | 0.1387483 | 0.976 | 993.4382 | 0.5493713 | 0.9969994 | 742.5371 | 0.410623 | ||
| ### Expor | ting data |
## Rows: 107 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.